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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Author

Listed:
  • Maria Prosperina Vitale

    (University of Salerno)

  • Giuseppe Giordano

    (University of Salerno)

  • Giancarlo Ragozini

    (University of Naples Federico II)

Abstract

In the present contribution we provide a discussion of the paper on “Bayesian graphical models for modern biological applications”. The authors present an extensive review of Bayesian graphical models, which are used for a variety of inferential tasks applied to biology and medicine settings. Our contribution proposes a conceptual connection between two scientific frameworks, graphical models and social network analysis, by highlighting also the role played by network models and random graphs. A bibliometric analysis is performed by exploiting publications collected from online bibliographic archives to map the main themes characterizing the two research fields. Specifically, a co-word network analysis is carried out using visualization tools and thematic evolution maps.

Suggested Citation

  • Maria Prosperina Vitale & Giuseppe Giordano & Giancarlo Ragozini, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 269-278, June.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:2:d:10.1007_s10260-021-00603-4
    DOI: 10.1007/s10260-021-00603-4
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    3. Joshua Daniel Loyal & Yuguo Chen, 2020. "Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic," International Statistical Review, International Statistical Institute, vol. 88(2), pages 419-440, August.
    4. Vladimir Batagelj & Monika Cerinšek, 2013. "On bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 845-864, September.
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